Age related macular degeneration (AMD) is a leading cause of blindness. More specifically, AMD) is a medical condition usually affecting older adults that results in vision loss in the center of the visual field (the macula) because of damage to the retina. AMD is a major cause of visual impairment in older adults (>50 years). Macular degeneration can make it difficult or impossible to read or recognize faces, though often there remains enough peripheral vision to allow other activities of daily life.
FIG. 1 is a schematic drawing of the cellular components of the retina showing the glia and neurons. The different cell types are situated in a standard large mammalian retina and are designated in FIG. 1 using the following abbreviations: amacrine cells (A), astrocytes (AS), bipolar cells (B), cones (C), ganglion cells (G), horizontal cells (H), Müller cells (M), microglia (Mi), rods (R), and cones (C). Note the interactions between the cells and blood vessels (BV). Note also the location of the different layers of the retina from the most internal to the outermost layers: the innermost optic nerve (ON), nerve fibre layer (NFL), ganglion cell layer (GCL), inner plexiform layer (IPL), inner nuclear layer (INL), outer plexiform layer (OPL), outer nuclear layer (ONL), outer segment layer (OS), pigment epithelium (PE), and the outermost choroid (Ch). [FIG. 1 is reproduced from Vecino, Elena, et al. “Glia-neuron interactions in the mammalian retina.” [Progress in retinal and eye research (2015)].
The inner layer of the eye is the retina and comprises a number of layers. Behind the retina is the choroid which contains the blood supply to all three layers of the eye, including the macula which is the central part of the retina that surrounds the optic disc. AMD occurs in “dry” and “wet” forms. In the dry (nonexudative) form, cellular debris called drusen accumulates between the retina and the choroid, and the retina can become detached. In the wet (exudative) form which are more severe, blood vessels grow up from the choroid behind the retina, and the retina can become detached. It can be treated with laser coagulation, and with medication that stops and sometimes reverses the growth of blood vessels.
Early detection and prediction of AMD can reduce the incidence of blindness. Pathological changes in different retinal tissue layers (such as drusens, retinal pigment epithelium (RPE) abnormalities, etc.) are the indication of early stages of AMD. Retinal imaging is mainly used for the diagnosis of AMD, and has evolved rapidly during the last 160 years to the extent it is now widely used for clinical care and management of patients with retinal as well as systemic diseases. Retinal fundus photography and optical coherence tomography (OCT) are the leading retinal imaging technologies in current use.
Retinal fundus photography is defined as the process whereby a two-dimensional (2-D) representation of the three-dimensional (3-D) retinal semi-transparent tissues projected onto the imaging plane is obtained by using reflected light. Optical coherence tomography (OCT) is an established medical imaging technique that uses light to capture high resolution and three-dimensional images of optical scattering media (for example, the retina). Optical coherence tomography is based on low-coherence interferometry, typically employing near-infrared light. The use of relatively long wavelength light allows it to penetrate into the scattering medium.
Projection optical coherence tomography (OCT) fundus images can provide enhanced visualization of different retinal layers which is very useful for the early prediction of AMD [(see Gorczynska, Iwona, et al. in the reference listing below). Projection OCT fundus images are generated from ultrahigh-resolution OCT images. But ultrahigh resolution OCT imaging technology is very expensive and not available in many remote and rural areas. Embodiments of these teachings provide a more cost-effective technique to predict AMD than OCT imaging.
In this regard the following references are relevant:                Gorczynska, Iwona, et al. “Projection OCT fundus imaging for visualising outer retinal pathology in non-exudative age-related macular degeneration.” [BRITISH JOURNAL OF OPHTHALMOLOGY 93.5 (2009): 603-609].        Kandel, Benjamin M., et al. “Predicting cognitive data from medical images using sparse linear regression.” [INFORMATION PROCESSING IN MEDICAL IMAGING; Springer. Berlin, Heidelberg, (2013).] proposes a sparse linear regression model to estimate cognitive data from Magnetic Resonance Imaging (MRI).        Yang, Jimei, et al. “Weakly-supervised disentangling with recurrent transformations for 3d view synthesis.” [ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS. (2015)] proposes a recurrent convolutional encoder-decoder network to synthesize novel views of a 3D object from a single image.        Fischer, Philipp, et al. “FlowNet: Learning Optical Flow with Convolutional Networks.” [ARXIV PREPRINT ARXIV:1504.06852 (2015)] presents two architecture of convolutional neural network for estimating optical flows—one architecture is the generic architecture and other uses a specific layer that correlates feature vectors at different image locations.        Gregor, Karol, et al. “DRAW: A recurrent neural network for image generation.” [ARXIV PREPRINT ARXIV: 1502.04623 (2015)] describes a Deep Recurrent Attentive Writer (DRAW) neural network architecture for image generation. DRAW networks combine a spatial attention mechanism that mimics the foveation of the human eye, with a sequential variational auto-encoding framework that allows for the iterative construction of complex images to yield a deep convolutional neural network based auto-encoder.        Masci, Jonathan, et al. “Stacked convolutional auto-encoders for hierarchical feature extraction.” [ARTIFICIAL NEURAL NETWORKS AND MACHINE LEARNING-ICANN 2011, p 52-59. Springer. Berlin, Heidelberg (2011)].        Stacked Denoising Auto en coders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion[Vincent (2010)].        Contractive Auto-Encoders: Explicit Invariance During Feature Extraction[Rifai (2011)].        Stacked Convolutional Auto-Encoders for Hierarchical Feature Extraction[J. Masci (2011)].        Vecino, Elena, et al. “Glia-neuron interactions in the mammalian retina.” [PROGRESS IN RETINAL AND EYE RESEARCH (2015)].        Nowak, Eric, Frederic Jurie, and Bill Triggs. “Sampling strategies for bag-of-features image classification.” [COMPUTER VISION-ECCV 2006, page 490-503. Springer. Berlin, Heidelberg (2006)].        